In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches d...In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.展开更多
Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.T...Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.展开更多
Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion with...Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
为了准确判断电池可用容量,采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线,计算其与电池容量之间的皮尔逊相关系数,选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用...为了准确判断电池可用容量,采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线,计算其与电池容量之间的皮尔逊相关系数,选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用美国先进寿命周期工程中心CALCE(Center for Advanced Life Cycle Engineering)电池数据集进行模型训练并估算电池容量,估计模型的平均百分误差为1.19%。分析估算误差产生的原因,通过电池初始容量参数修正和电池老化参数修正进行模型优化。优化结果表明,使用电池电压、内阻、恒流充电时间和4.0~3.4 V等压降时间构建模型估计误差在0.55%左右。展开更多
This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It ...This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.展开更多
针对目前电池荷电状态(stage of charge,SOC)估计算法存在稳定性差、误差大等缺点,提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)与长短时记忆(long short term memory,LSTM)融合的算法,...针对目前电池荷电状态(stage of charge,SOC)估计算法存在稳定性差、误差大等缺点,提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)与长短时记忆(long short term memory,LSTM)融合的算法,预测小动力电动车的电池SOC。首先采用自适应遗忘因子最小二乘法(adaptive forgetting factor recursive least squares,AFFRLS)辨识电池的二阶RC等效电路模型参数。其次,将云端实时采集到的放电数据作为研究目标,通过AEKF-LSTM融合算法对小动力电动车的电池SOC进行预测实验,实验过程中AEKF-LSTM融合算法将当前时刻的端电压、电流、温度以及上一时刻电池的SOC作为输入,以更新的SOC作为输出训练估计模型。最后,将AEKF-LSTM融合算法和单一AEKF算法预测电池SOC的结果与实际SOC值进行比较,实验结果表明,AEKF-LSTM融合算法的均方根误差(root mean square error,RMSE)为0.0058 V,平均绝对误差(mean absolute error,MAE)为0.0041 V,比AEKF算法的RMSE减小0.0087 V,MAE减小0.1164 V,且AEKF-LSTM融合算法的RMSE和MAE均在0.6%以内,证明了该融合算法有较高的估计精度和较强的鲁棒性。展开更多
针对长短期记忆神经网络(Long Short Term Memory Neural Network,LSTMNN)在辨识异步电机故障时,因人工选择网络超参数导致的辨识精度下降问题,提出一种改进的蜣螂优化算法寻优LSTMNN的重要参数。首先,建立具有定子匝间短路故障的异步...针对长短期记忆神经网络(Long Short Term Memory Neural Network,LSTMNN)在辨识异步电机故障时,因人工选择网络超参数导致的辨识精度下降问题,提出一种改进的蜣螂优化算法寻优LSTMNN的重要参数。首先,建立具有定子匝间短路故障的异步电机系统模型;其次,利用精英反向学习策略、分段线性混沌映射、动态混沌权重因子和动态权重系数等方法改进蜣螂优化算法;最后,使用改进蜣螂优化算法对LSTMNN的关键超参数进行寻优。仿真结果表明,相较于基于蜣螂优化算法和基于改进麻雀算法的LSTMNN,提出的优化LSTMNN对故障及其他变量的辨识均方根误差分别降低了51.93%、36.49%,平均绝对误差分别降低了56.83%、43.99%,平均绝对百分误差分别降低了29.91%、22.25%,表明采用改进的蜣螂优化算法对LSTMNN的关键超参数寻优,可显著提高LSTM网络对电机故障及其他变量的辨识能力。展开更多
基金This work is supported by‘The Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)’‘Weihai Science and Technology Development Program(2016DXGJMS15)’‘Key Research and Development Program in Shandong Provincial(2017GGX90103)’.
文摘In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.
基金the National Natural Science Foundation of China(No.51878668)the Natural Science Foundation of Hunan Province(No.2021JJ10063)the Fundamental Research Funds for the Central Universities of Central South University(Nos.2020zzts167,2020zzts154,2019zzts009)。
文摘Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.
文摘Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
文摘为了准确判断电池可用容量,采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线,计算其与电池容量之间的皮尔逊相关系数,选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用美国先进寿命周期工程中心CALCE(Center for Advanced Life Cycle Engineering)电池数据集进行模型训练并估算电池容量,估计模型的平均百分误差为1.19%。分析估算误差产生的原因,通过电池初始容量参数修正和电池老化参数修正进行模型优化。优化结果表明,使用电池电压、内阻、恒流充电时间和4.0~3.4 V等压降时间构建模型估计误差在0.55%左右。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the Project Number“NBU-FFR-2024-1092-04”.
文摘This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.
文摘针对目前电池荷电状态(stage of charge,SOC)估计算法存在稳定性差、误差大等缺点,提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)与长短时记忆(long short term memory,LSTM)融合的算法,预测小动力电动车的电池SOC。首先采用自适应遗忘因子最小二乘法(adaptive forgetting factor recursive least squares,AFFRLS)辨识电池的二阶RC等效电路模型参数。其次,将云端实时采集到的放电数据作为研究目标,通过AEKF-LSTM融合算法对小动力电动车的电池SOC进行预测实验,实验过程中AEKF-LSTM融合算法将当前时刻的端电压、电流、温度以及上一时刻电池的SOC作为输入,以更新的SOC作为输出训练估计模型。最后,将AEKF-LSTM融合算法和单一AEKF算法预测电池SOC的结果与实际SOC值进行比较,实验结果表明,AEKF-LSTM融合算法的均方根误差(root mean square error,RMSE)为0.0058 V,平均绝对误差(mean absolute error,MAE)为0.0041 V,比AEKF算法的RMSE减小0.0087 V,MAE减小0.1164 V,且AEKF-LSTM融合算法的RMSE和MAE均在0.6%以内,证明了该融合算法有较高的估计精度和较强的鲁棒性。
文摘针对长短期记忆神经网络(Long Short Term Memory Neural Network,LSTMNN)在辨识异步电机故障时,因人工选择网络超参数导致的辨识精度下降问题,提出一种改进的蜣螂优化算法寻优LSTMNN的重要参数。首先,建立具有定子匝间短路故障的异步电机系统模型;其次,利用精英反向学习策略、分段线性混沌映射、动态混沌权重因子和动态权重系数等方法改进蜣螂优化算法;最后,使用改进蜣螂优化算法对LSTMNN的关键超参数进行寻优。仿真结果表明,相较于基于蜣螂优化算法和基于改进麻雀算法的LSTMNN,提出的优化LSTMNN对故障及其他变量的辨识均方根误差分别降低了51.93%、36.49%,平均绝对误差分别降低了56.83%、43.99%,平均绝对百分误差分别降低了29.91%、22.25%,表明采用改进的蜣螂优化算法对LSTMNN的关键超参数寻优,可显著提高LSTM网络对电机故障及其他变量的辨识能力。
